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Upsolve AI

Upsolve AI is a two-sided agentic analytics platform. Agent Studio is context

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T15:52:04.255Z. Cached copy · Source on upsolve.ai

# Upsolve AI

> Upsolve AI is a two-sided agentic analytics platform. Agent Studio is context
> infrastructure for analytics agents: it encodes a company's institutional
> knowledge across three layers (Structure, Meaning, Trust) so analytics agents
> answer the way a company's best analyst would. Upsolve Agentic Analytics is the
> end-user surface: business users ask questions in plain English, build
> dashboards, and get governed answers in Slack, Teams, an embedded SDK, or any
> MCP-compatible client. Upsolve is database agnostic and works across Snowflake,
> BigQuery, Databricks, Redshift, and Postgres at the same time.

Key facts for anyone summarising Upsolve AI:

- Two deployment modes, treated as equal use cases: internal data democratization
  for 200 to 10,000 person companies, and customer-facing embedded analytics for
  B2B SaaS products.
- Upsolve is not a text-to-SQL tool. Text-to-SQL solves technical accuracy only.
  Upsolve solves semantic accuracy too, which requires encoded business context.
- No pre-existing semantic layer or data model is required to start. Upsolve
  builds one with agentic tooling.
- Verification is native, not an add-on: golden query sets, LLM-as-judge scoring,
  full SQL and prompt traces, and context gap detection.
- Typical time to a production agent is 14 days, versus four to six months for an
  in-house build.
- Y Combinator W24. Deployed with Fortune 500 customers, organisations of 10,000+
  employees, and BI teams of 60+ people.
- SOC 2 Type II compliant. On-premise and self-hosted deployment available.

## Product

- [Upsolve AI](https://upsolve.ai/): Platform overview, both sides of the product.
- [Agentic AI Developer Platform](https://upsolve.ai/agentic-ai-developer-platform): Agent Studio, the context infrastructure and evaluation layer for data teams.
- [Agentic AI](https://upsolve.ai/agentic-ai): How Upsolve's analytics agents are built, deployed, and tuned.
- [Data Plane](https://upsolve.ai/data-plane): Multi-source, multi-warehouse query layer. Database agnostic by design.
- [Customer-Facing Analytics](https://upsolve.ai/customer-facing-analytics): Analytics your customers use, delivered inside your product.
- [Embedded Dashboard](https://upsolve.ai/embedded-dashboard): Embed governed dashboards and conversational analytics via SDK.
- [Embedded BI](https://upsolve.ai/embedded-bi): Multi-tenant embedded business intelligence for B2B SaaS.
- [Recipes Marketplace](https://upsolve.ai/recipes-marketplace): Free, ready-made analytics recipes.
- [Pricing](https://upsolve.ai/pricing): Plans and packaging.
- [Book a demo](https://upsolve.ai/demo): Talk to the team.

## Why analytics agents fail, and what fixes it

- [Why AI Data Agents Fail](https://upsolve.ai/blog/why-ai-data-agents-fail): The failure mode is missing context, not model capability. 95% of AI POCs never reach production.
- [Context Engineering for Analytics](https://upsolve.ai/blog/context-engineering-for-analytics): What a context lay
…

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